000 03301nam a22005175i 4500
001 978-3-031-01543-4
003 DE-He213
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007 cr nn 008mamaa
008 220601s2007 sz | s |||| 0|eng d
020 _a9783031015434
_9978-3-031-01543-4
024 7 _a10.1007/978-3-031-01543-4
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aVlassis, Nikos.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979557
245 1 2 _aA Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
_h[electronic resource] /
_cby Nikos Vlassis.
250 _a1st ed. 2007.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2007.
300 _aXII, 71 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
505 0 _aIntroduction -- Rational Agents -- Strategic Games -- Coordination -- Partial Observability -- Mechanism Design -- Learning.
520 _aMultiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering the theoretical foundations as well as more recent developments in a coherent and readable manner. The text is centered on the concept of an agent as decision maker. Chapter 1 is a short introduction to the field of multiagent systems. Chapter 2 covers the basic theory of singleagent decision making under uncertainty. Chapter 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium. Chapter 4 deals with the fundamental problem of coordinating a team of collaborative agents. Chapter 5 studies the problem of multiagent reasoning and decision making under partial observability. Chapter 6 focuses on the design of protocols that are stable against manipulations by self-interested agents. Chapter 7 provides a short introduction to the rapidly expanding field of multiagent reinforcement learning. The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_979558
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
710 2 _aSpringerLink (Online service)
_979559
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031004155
776 0 8 _iPrinted edition:
_z9783031026713
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_979560
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01543-4
912 _aZDB-2-SXSC
942 _cEBK
999 _c84800
_d84800